SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 926–950 of 1963 papers

TitleStatusHype
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Deep kernel processes—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Infinite attention: NNGP and NTK for deep attention networks—0
Bayesian Optimization with Tree-structured Dependencies—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Information fusion in multi-task Gaussian processes—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Information Theoretic Meta Learning with Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction—0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified